Currently, digital images are derived through various devices including digital cameras and the digital scanning of film images. Many times the sharpness of an image is degraded by optical elements or by irregularities in the image sensor. For these reasons, it is often desirable to sharpen an image after it has been converted to a digital form. Conventional sharpening methods, such as unsharp masking, achieve the appearance of edge sharpening by locally lightening the lighter portion of an edge region and locally darkening the darker portion of an edge region. The resulting increase in microcontrast provides the sharpening effect. Such methods can be applied to black and white digital images as well as to colored digital images.
Referring to FIG. 1A, a one dimensional trace of an edge profile is shown in which higher code values correspond to lighter shades and lower code values to darker shades. In FIG. 1B, the same edge profile has been further blurred in accordance with the prior art technique of unsharp masking. The curve in FIG. 1B is subtracted from the curve in FIG. 1A and the resulting curve shown in FIG. 1C in which the amplitudes P and N, for positive and negative boost respectively, are approximately the same size. The difference curve of FIG. 1C is added to the original curve in FIG. 1A and this final curve, shown in FIG. 1D, depicts the profile of the sharpened edge. Although unsharp masking was originally a film technique, it also has a digital version. Shown in FIGS. 2, 3A and 3B are examples of boost kernels which, when applied to a digital image, directly produce boost values analogous to those shown in FIG. 1C and sharpened edges analogous to those shown in FIG. 1D.
When these conventional sharpening methods are applied to noisy images, high spatial frequency boost kernels (such as the 3.times.3 kernel shown in FIG. 2) tend to amplify the noise as well as image edge features. Excessive noise amplification may be reduced by using a mid-range spatial frequency boost kernel (such as the 5.times.5 kernels shown in FIGS. 3A and 3B), however, image edge features are primarily enhanced in the mid-range of spatial frequencies.